Sinem EYİCE BAŞEV Bahçeşehir Üniversitesi, İletişim...

Global Media Journal: TR Edition 5 (9)

Eyice Başev Fall 2014

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ONLINE BUYING BEHAVIOUR OF TURKISH CONSUMER: AN EXPLORATORY

STUDY ON Hepsiburada.com

Sinem EYİCE BAŞEV

Bahçeşehir Üniversitesi,

İletişim Fakültesi, Reklamcılık Bölümü,

İstanbul

ABSTRACT

The aim of the article was to investigate online shopping behaviour of Turkish consumers in the

context of Hepsiburada.com by addressing “What factors do determine online shopping behaviour of

Turkish consumers?” To this end, the focus on this article was exploring the influence of socio-

economic, beliefs of individuals, their attitudes, subjective norms and perceived behavioural control

on the utilisation of Hepsiburada.com’s online store. The methodological ground of this study was

developed according to realism and a deductive reasoning was followed in rationalising the research

process. Qualitative and quantitative methods were incorporated in data collection process. 60

respondents took part in the research, while 10 of them were interviewee and 50 of them responded

through questionnaires. The participants for the interviews were selected according to their

convenience to the author and the respondents for the questionnaires were selected through snowball

sampling with the help of the interviewees. The findings were derived with the help of “describe,

compare and relate” model used for qualitative information and correlation and regression analyses

were used for the analysis of quantitative data. The findings of this study showed that age variable was

significant in demographic factors. Innovativeness in terms of previous experience and satisfaction

with computer and internet usage also found important. Findings implied that the Theory of Planned

Behaviour can explain Turkish consumers’ online shopping behaviour which means their actual

behaviour is shaped by their attitudes toward online shopping where attitudes emerge in the form of

compatibility with the lifestyle of consumers.

Keywords: online consumer behaviour, consumer behaviour, hepsiburada.com

Türk Tüketicilerin Çevrimiçi Alışveriş Davranışları: Hepsiburada.com Üzerine Bir Araştırma

ÖZET

Bu makalenin amacı Türk müşterilerin internet üzerindeki alışveriş davranışlarını Hepsiburada.com

kapsamında incelemektir. Bu anlamda araştırmanın temel sorusu Türk müşterilerin internet üzerindeki

alışveriş davranışını hangi faktörler etkiler seklinde düzenlenmiştir. Bu bağlamda bu makalenin odak

alanı sosyo ekonomik faktörlerin, müşterilerin inançlarının, davranışlarının, sosyal kuralların

Hepsiburada.com tercihlerindeki etkisini araştırmaktır.

Bu çalışmada yöntem olarak Realism seçilmiş olup tümden gelim yöntemi uygulanmıştır. Kantitatif ve

kalitatif olmak üzere iki veri çeşidi de toplanmıştır. 60 katılımcı bu araştırmaya katılmış olup 10 tanesi



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yüz yüze görüşmeye dahil olmuştur 50 tanesi ise anket çalışmasına dahil olmuştur. Yüz yüze görüşme

için seçilen örneklem uygunluk yöntemine göre belirlenmiştir. Anket katılımcıları ise kartopu

örneklem yöntemine göre seçilmiştir. Kalitatif datanın analizinde açıkla ve kıyasla metodu kullanılmış

olup kantitatif datanın analizinde ise korelasyon ve regresyon analizleri kullanılmıştır. Çalışmanın

sonuçları Türk müşteriler arasında yaşın internet üzerindeki alışveriş davranışının belirlenmesinde

önemli bir faktör olduğunu ortaya koymuştur. Bununla birlikte yenilikçilik ve bilgisayar kullanım

deneyimlerinin de internet üzerinde alışveriş davranışının belirlenmesinde önemli faktörler oldukları

ortaya konmuştur. Ancak çalışmanın esas önemli bulgusu Türk müşterilerin internet üzerindeki

alışveriş davranışlarının daha çok Theory of Planned Behaviour ile açıklanabileceği bu bağlamda

kültürün önemli bir faktör olarak ortaya çıktığı sonucudur.

Anahtar kelimeler: online tüketici davranışı, tüketici davranışı, hepsıburada.com

Introduction

The ultimate aim of the study is to investigate adoption of internet shopping by

Turkish consumers in the context of Hepsiburada.com. In this context, the author identifies

factors which determine online buying behaviour of Turkish consumers, explores the

influence of socio-economic and cultural characteristics of Turkish consumers on their

purchases over the internet and examines Turkish consumers’ online shopping behaviour by

scrutinising the influence of beliefs of individuals, their attitudes, subjective norms and

perceived behavioural control on the utilisation of Hepsiburada.com’s online store.

In this context, the author makes an attempt to answer the research question of “What

factors do determine online shopping behaviour of Turkish consumers?”. In finding the

answers to this question, the following research questions are used to guide the researcher:

What factors do encourage/ prevent Turkish consumers from buying goods over the

internet?

What are the role of demographic factors and cultural characteristics of Turkish

consumers on their purchases over the internet?

In what way do subjective norms, attitudes, behavioural intentions and perceived

behavioural control affect Turkish consumers’ actual purchases from

Hepsiburada.com?

Review of the Literature Review

Online Consumer Behaviour

Solomon (1999) defines the study of consumer behaviour as investigation into the

sequence of actions and associated events surrounding selection, purchase, consumption and

disposal of goods and services with the aim of satisfying the needs and wants of an individual

or a group. Goldsmith and Bridges (2000) argue that from this perspective a definition based



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on the collection of product information arising from exposure to associated online

advertising initiatives, the search for specific information about a product, brand comparison

activities and eventual completion of an online transaction can be applied to online consumer

behaviour.

Theories of Online Consumer Behaviour

The classification term rational behaviour theory is most often applied to theories of

online consumer behaviour attempting to characterise and predict individual behaviour in an

online shopping environment.

There is no shortage of study results on this topic but there are two theories that have

emerged as the most widely accepted – the theory of planned behaviour and the model of

technology acceptance. Both theories are discussed in detail in the following chapters of this

research paper.

Technology Acceptance Model

The technology acceptance model (TAM) is developed by Davis et al. (1989) as a

framework user acceptance of information technology. The model speculates that behavioural

intention to use has a direct impact on the use of systems which is also influenced by user

perceptions of system utility and attitudes regarding use of the system. Perceptions of ease of

system use also influence user attitudes and perceptions of utility. The reflection of an

individual’s significant belief in use of a particular technology system facilitating improved

performance is the foundation of perception of usefulness. Taylor and Todd (1995) define

perceived usefulness as an individual’s belief that using the technology will not require

significant investments of effort. Hernandez et al. (2011) argue that researchers focus on

‘perceived ease of use and usefulness’ in an effort to explain behaviour.

Theory of Planned Behaviour

The theory of planned behaviour (TPB) is a model of human behaviour based on

cognitive aspects (Ajzen, 1985). The prediction of identified behaviour and development of

clear insight into those behaviours is the model’s primary purpose. According to Fishbein and

Ajzen (1975), an understanding of ‘intention’ can be derived from qualification that behaviour

is impacted by factors including attitude, subjective norms and varying degrees of cognition.

Oliver (1997) suggests defining intention as the specified probability of performing a

particular action.



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Figure 1: Components of TPB

Source: Adapted from Liang and Lim (2011)

Under the premises of TPB actual individual behaviour with regards to certain actions

is directly impacted by the individual’s behavioural intention which is determined by a

combination of factors including attitude, subjective norms and the perceived degree of

behavioural control in carrying out a specific action. The strength of willingness to commit

the required effort to perform a particular action is the measure of behavioural intention

(Liang and Lim, 2011).

Factors Influence Online Shopping Behaviour

A large body of research has been conducted investigating motivational factors driving

consumers to shop at retail outlets. Babin et al. (1994) identify two primary factors behind

why people shop – for pleasure, also referred to as hedonic shopping and in order to satisfy a

specific want or need, referred to as utilitarian shopping. Wolfinbarger and Gilly (2001)

conducted qualitative analysis delivering results supporting the two drivers behind why

people shop and defining hedonistic shoppers as experiential purchasers who obtain

satisfaction from a combination of the actual purchase transaction and shopping experiences

such as those offered on auction and hobby sites.

Use of online shopping because of its convenience, ease of search for product

information, variety of merchandise available and control over overall experience are

characteristic of utilitarian shoppers. Alreck and Settle (2002) argue that online shopping is

regarded as faster by consumers in comparison to traditional channels. Bhatnagar et al. (2000)



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illustrated that online purchases are made more frequently when consumers believe the online

channel to be more convenient.

Ramaswami et al. (2001) argue that irrespective of the time saving advantages of

online shopping those who typically search online for financial products for example have a

tendency to conduct online transactions anyway.

Bellman et al. (1999) conducted studies investigating the type of person who conducts

online transactions and demonstrated the limited effect of demographic aspects (i.e. income,

age and education) on the online shopping process. The key factor driving intention to

purchase was whether a previous online purchasing decision or other online transaction had

been made. Individual decisions around whether to continue to spend time online once it has

been started is heavily influenced by levels of satisfaction from the experience and amount of

time available.

Korgaonkar and Wolin (1999) conducted research investigating internet usage and

online purchasing and identified an alternative group of factors influencing the decision to

shop online including avoidance of social interaction, concerns regarding security of online

transactions, information availability, and control over interactions, community interaction,

personal privacy and financial aspects.

According to Korgaonkar and Wolin (1999), using the aspect of control regarding the

existence of previous purchases as a measure demonstrated that concerns around security of

online transactions, interactive control, ability to interact with an organisation and financial

aspects were the most common factors in determining likelihood to purchase online.

Joines et al. (2003) conducted additional research into this subject investigating the

impact of each identified factor on the amount of time spent online seeking out goods and

services and the frequency with which consumers purchase investment, travel and computer-

related products via an online transaction. Their results indicated a slightly statistically

significant relationship between those concerned with expenses or online transaction security

and time spent conducting searches on the Internet. It was discovered that the amount of

available product information, interactive control, financial drivers and security concerns all

impacted the prevalence of online purchases.

Self Efficacy

Self-efficacy is the degree to which an individual believes that personal performance

of a specific action will deliver anticipated results (Bandura, 1977). From the perspective of



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online purchasing, self-efficacy reflects levels of consumer confidence in terms of security

and comfort in using the internet to search for information and conduct an online transaction.

Im et al. (2008) claim that from the self-efficacy perspective online purchasing can be

explained by prior internet usage given the fact that individual attitudes are formed on the

basis of previous interaction and experiences of shopping online.

Miyazaki and Fernandez (2001) emphasise the significance of prior internet

experience on development of individual perceptions of risk associated with online

transactions and maintain that individuals with prior internet shopping experience have a less

risk-focused perspective on shopping over the internet and simultaneously improve levels of

satisfaction which stimulates the undertaking of further online purchases.

Bigne et al. (2008) and Hernandez et al. (2011) are in agreement regarding the

prediction of consumer online purchase behaviour using acceptance, development of

consumer insight into information technology systems, the Internet and prior consumer

experience.

Innovativeness

Hirschman (1980) proposed innovativeness as a variable indicating acceptance and

individual consumption of new products, concepts and so on. Rangaswamy and Gupta (1999)

claim that with regards to internet usage there is a key difference between online purchasing

behaviour of individuals and state that those who generally shop more also spend more time

on the Internet.

Support for the significance of innovativeness in explaining online consumer behaviour exists

in empirical studies. Goldsmith (2001) demonstrated, for example, the existence of a positive

relationship between internet usage and levels of innovation. However, Dahlen (2002)

illustrated that with increasing individual internet experience, changes occur in online search

and behaviour so those with more experience are able to find information more quickly, visit

a limited number of websites and overall spend less time online.

According to Novak et al. (2000), as individuals with higher levels of the internet

experience become aware of this their levels of satisfaction from online experiences also

increases. Park and Kim (2003) argue that individuals who derived satisfaction from online

surfing will develop more positive attitudes towards purchasing online.

Socio-economic Factors

Li and Kirkup (2007) claim socio-economic factors have significant influences on

individual internet usage and online transaction decisions. Yu et al. (2005) deliver findings



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showing there are differences in terms of information technology use between experienced

users which come from the fact that increasing experience changes the influence of features

on usage of information systems. Sun and Zhang (2006) claim that from this perspective there

may be a decrease in the significance of socio-economic factors. This statement is supported

by empirical evidence. Anandarajan et al. (2000) discovered a lack of association between age

and gender and Internet usage in the workplace. Available evidence suggests a need for

further testing to determine the extent of the impact of socioeconomic factors on online

purchasing behaviour.

Age

Harrison and Rainer (1992) point to the body of literature highlighting the significant

impact of age as a factor determining individual behaviour while using information systems.

Despite this, there are relatively few studies examining the relationship between demographic

variables such as age and alternative explanations of motivational aspects and attitudes

towards purchasing online. Hubona and Kennick (1996) demonstrated the ease with which

computer skills are developed by young people.

Gender

It has been claimed that as an influential variable gender has a determining role with

respect to use of information systems. Sun and Zhang (2006) demonstrate higher levels of

practicality amongst males compared to females and show that female behaviour is influenced

by higher levels of concern around the performance of a new task and concern with

environmental aspects. Such factors hint at the effectiveness of gender in establishing ease of

use, perceived usefulness, self-efficacy and individual innovativeness. Despite these factors

with respect to the impact of gender on internet usage there is evidence to the contrary which

exists.

Income

Income is the final socioeconomic variable associated with acceptance with regards to

technology. Chau and Hu (2002) claim the existence of a negative association between levels

of income and individual perception of risk leading to increased online purchases. Risk

perception increases amongst the low income segment which has the effect of discouraging

online purchases. Hernandez et al. (2011) point out that the potential for financial loss from

online activities drives the significance on income as a variable influencing internet usage and

online purchase behaviour.



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Risk

Kuisma et al. (2007) claim online purchase behaviour is influenced by perceived risk

and identified five categories of risk relating to online shopping which can be classified as

“security/privacy, financial, social time/convenience and performance loss”.

Kuisma et al. (2007) define security/privacy as concerns relating to possible loss due

to fraudulent activity or breach of bank security through exposure to hacker activity. The

definition of financial risk encompasses financial loss resulting from transactional mistakes

and through misuse of bank account information. The risk of being subject to the disapproval

of friends/family/work colleagues as a result of using online banking services is the essence of

social risk. Individuals believe that depending on how online banking is perceived amongst

influential social groups this may enhance or diminish social status.

It is possible that members of different social groups hold both positive and negative

views of shopping online that will consequently influence their views of those who adopt it or

alternatively do not adopt it.

Time/convenience risk refers to time-lost and inconvenience experienced as a result of

payment delays, problems with site navigation and difficulty in locating relevant hyperlinks

and services.

Performance risk is defined as losses sustained as a result of inadequacies of the online

shopping service or website malfunctions. According to Kuisma et al. (2007), server

communication errors or internet service disruptions while processing online transactions

foster apprehension amongst consumers as such interruptions may cause the incurring of

unanticipated losses.

Trust

Trust is a key element of considerably higher significance in online than offline

environments. Online environments do not provide opportunities for direct physical contact

between parties and merchandise is not able to be inspected prior to purchase. In an online

environment it is not clear to potential purchasers whether the retailer is the actual owner of

the merchandise and retailers are not always guaranteed that payment will be received. In an

online environment there is a high level of uncertainty for both parties. Traditional retailer

outlets provide the opportunity to inspect merchandise before purchase, pay for it and leave as

the new owner of the merchandise.



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Retailers have the ability to check buyer payments immediately. In physical retail

outlets, trust is not as much of concern. However, with respect to online interactions there

would be no internet purchases transacted without a degree of trust from both parties.

Rousseau et al. (1998) define trust as a cognitive state based on the intention to accept

a degree of vulnerability with respect to anticipated positive outcomes resulting from the

intention to behaviour of another party which portrays trust as an attitude stimulating risk

taking due to expected positive outcomes.

Ganesan (1994) suggests that as consumers gain insight through increased experience

with online purchasing, trust develops alongside experience but trust is not borne solely of

experience. Ganesan (1994) maintains that levels of perceived satisfaction from previous

online experiences play a significant role. Consumers with higher levels of favourable online

purchasing experiences will perceive electronic retailing as a trustworthy service.

Methodology

In carrying out this research, the author took realistic stance and followed deductive

reasoning in rationalising the research process. In the data collection process, the author

incorporated qualitative and quantitative methods and utilised semi-structured interviews and

structured questionnaires for the data gathering process. 60 respondents took part in the

research, while 10 of them (who were identified according to their convenience to the

researcher were interviewed) and 50 of them (who were selected through a snowball sampling

with the help of interviewees) responded through questionnaires. Incorporating qualitative and

quantitative research methods allowed the author to develop an in-depth understanding of the

consumers’ preferences but at the same measuring the effect with the help of statistical

analyses.

Data Sources

This study employed both primary and secondary data. The research started with the

collection of information regarding relevant theories about consumer behaviour, consumer

shopping behaviour, online shopping behaviour to form the literature review. In gathering the

secondary data, the author used various books and articles specialised in these areas. The

main sources of secondary data were academic journals and the books published on online

consumer behaviour.



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The author conducted article research on the Emerald’s website and for the books, she

used the libraries in Istanbul. To answer the research questions, primary data was also

gathered.

Interviews

Among the qualitative methods, interviews were selected for the first part of the

primary data collection. For the interviews, the author preferred semi-structured interviewing

technique because in this method, the respondents were given the freedom the state their

experiences in their own words.

For the interviews, the author prepared 16 questions in the open-ended questions (with

no pre-determined answers) concerning various issues ranging from computer usage, internet

usage, online shopping and shopping at Hepsiburada.com’s website.

For the interviews, the author utilised a non-probability sampling procedure and

selected those who were easily accessible to her. Despite the use of convenience sampling, the

author made good effort to include the views of diverse people in terms of their age, gender

and occupation so that the sample could reflect the characteristics of overall population. The

sample for the interviews included 10 consumers and their profile is summarised in the table

below.

The interviews were conducted during the last week of March 2014 and the first week

of April 2014. On average, an interview lasted around 30 to 45 minutes. The interviews were

carried out in Turkish and the answers of respondents translated back to English by the

author.

Table 1: Profile of the Interviewees



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Qualitative information has roots on interpretivist philosophy which involves

generating meaningful knowledge from the vast amount of narrative data in the case of

interviews. For this process, Bazeley (2009) suggests that analysis of qualitative data

necessitates “much deeper analysis” (p. 6) and offers the model of “describe, compare and

relate” for comprehensive and coherent evaluation of narrative data.

In this article, the author followed this model in the analysis of interviews and

interpreted the transcripts, classified them according to the relevance of emerging themes and

in the final stage, she related them with others by asking what causes certain behaviour to

occur or not to occur. This approached helped in developing in-depth understanding issues

related to online shopping behaviour of Turkish consumers.

Questionnaires

In addition to the interviews, this study incorporated questionnaires from the

quantitative data collection instruments. In this data collection method, the author used

structured method to facilitate gathering of quantifiable data.

Design of Questionnaires

In designing the questionnaire, the author utilised the topics covered in the interviews

but this time with the pre-determined answers. Since the underlying aim of questionnaires was

to investigate online buying behaviour Turkish consumers in the context of Hepsiburada.com.

The questionnaire included 24 questions.

Sample for Questionnaires

All consumers who reside in Turkey were the population of this study. Since the size

of the population was very big, the author utilised sampling process and determined the

participating Turkish consumers according to snowball sampling method which is defined as

“a non-probability sampling procedure in which subsequent respondents are obtained from

information provided by initial respondents” (Saunders et al., 2009, p.601). In selecting

respondents for the questionnaires, the author asked interviewed Turkish consumers to send

the questionnaire to their acquaintances.

Using this sampling procedure helped the author to reach 50 Turkish consumers in a

very short time which all included into the quantitative analysis. The survey questions were

designed in English, but they were translated into Turkish by the author.



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Questionnaire Procedure

Owing to utilising snowball sampling, the communication between the author and the

participants of the survey were indirect as the questionnaires were sent through the

interviewees. The author delivered the questionnaires to the interviewees via email which,

then, they distributed the questionnaires to the participants. Once the surveys filled out by the

participating consumers, they returned the questionnaires back to the author. This process is

depicted in the below figure.

Figure 2: Data Collection Process via Questionnaire

Analysis of Questionnaires

For the analysis of the data collected through questionnaires, the author employed

several statistical analyses in order to interpret data with the help of tables and figures. For the

analysis, the author used specific software called the Statistical Package for Social Science

(SPSS). To summarise the results, descriptive statistics were used. To measure the influence

of factors in shaping online shopping behaviour of Turkish consumers, the author employed

correlation and multiple regression analyses. The results produced by these statistical analyses

were valuable to this research in supporting significance of the findings and generalising

them.

Reliability and Validity

According to Collis and Hussey (2009), the reliability of research is related to the

study results, in that the results can be considered reliable if they could be duplicated by other

researchers performing an identical study. A statistical tool known as Cranach’s coefficient

alpha is used to measure internal consistency, which is the important factor in reliability



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(Pallant, 2005). A research is regarded as reliable when it has the score of more than 0.7

Cronbach’s coefficient alpha, and this indicates the degree to which there is an identical

fundamental attribute for each item on the scale. The Cronbach’s alfa for the data used in

regression analysis was 0.983 which means the measures used in the analysis were highly

reliable.

The validity of research is vital because this addresses the legitimacy of the study’s

results and conclusions. The results of qualitative research are only considered valid if the

evidence is carefully and sensitively gathered and analysed. The author ensured the validity of

the qualitative data by increasing trustworthiness of the information.

In this study, the author used most appropriate research methods for the investigation

of online buying behaviour of Turkish consumers and therefore she incorporated qualitative

and quantitative methods. As well as this, the author employed most relevant and

academically credible sources. The author also believes that participating respondents were

100% honest when replying the questions.

Analysis and Findings

This section presents analysis of information collected from interviews and

questionnaires carried out with Turkish consumers.

Analysis of Interviews

This section provides the findings from the interviews which carried out with ten

Turkish consumers. The intention was to develop an in-depth understanding about online

buying behaviour of Turkish consumers.

Computer and Internet Usage

This section is concerned with computer and internet usage behaviour of the

interviewees. Despite the differences in age, gender and occupation, all of the interviewed

consumers have been using computers more than a decade. Internet usage is not only limited

to work, consumers also use it for leisure mainly for watching movies, e-books and social

media interactions, for shopping and making payments. With regards to daily internet usage,

the average utilisation ranges from four to five hours per day, in most extreme cases it may

goes up to ten to eleven hours.

With regards to activities over the internet, the findings suggest that consumers spend

one to two hours for searching, one hour for professional purposes and one hour for social



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networking per day. However, when it comes to online shopping, the average usage is about

half an hour per week.

Online Shopping Behaviour

According to the interviews, the majority of the interviewed consumers shop online

with an exception of older male consumer. The most popular products which were bought

online were books and DVDs.

These were followed by clothing, flight or event tickets, home and business

equipments, electronic products and cosmetics. Underlying factors which encourage

consumers to shop online included time-saving, cheapness and difficulty in finding some

products in brick-and-mortar stores.

Advantages and Disadvantages of Online Shopping and Previous Experiences

The advantages and disadvantages of the online shopping were evaluated by the

interviewees. Among the advantages, easy access to wide product range, time saving, delivery

to straight to the door, inexpensiveness and being able to buy from all over the world.

Disadvantages of online shopping pronounced by the interviewees included credit card fraud

and lack of touch/try the product before purchasing.

On the other hand, the main concern of consumers when shopping online mainly

revolves around security issues because credit card fraud is very widespread problem in

Turkey. This is followed by the uneasiness caused by purchasing products without trying or

touching as they fear that products may not meet their expectations. The fearful part is the

gruelling returning process or getting back the refund.

Previous experiences of the interviewees indicated that online shopping was

satisfactory for consumers in most cases. However, long delivery time is the factor which

hinders general satisfaction of consumers.

Characteristics of Turkish Consumers’ Affecting their Online Shopping Behaviour

This section is concerned with determining Turkish consumers’ characteristics which

may affect their online shopping behaviour positively and negatively.

According to the interviewees, Turkish people are not familiar with internet very much

and they are not willing to make online payments as they do not trust e-tailers. Additionally,



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there are technological limitations which prevent consumers from shopping over the internet.

These factors adversely affect the adoption of online shopping.

On the other hand, inexpensiveness of products sold over the internet attracts price-

conscious and prudent Turkish consumers. Also consumers are attracted by time-efficiency

gained by shopping online.

Awareness of and Shopping from Hepsiburada.com

The interviewees were asked if they have shopped from Hepsiburada.com before or

they have heard about it. Findings from the interviews showed that about half of the

consumers did not shop from Hepsiburada.com’s website. In fact, some had no idea that

Hepsiburada.com is the Turkish’s one of the biggest online shopping store.

Those who shopped from Hepsiburada.com preferred buying books, DVDs, in

general. Product range and price are the two prominent factors which made consumers to shop

from Hepsiburada.com. However, trustworthiness of the website plays important role for the

consumers that is gained by the good reviews, good word-of-mouth, user-friendliness and

security measures to guarantee protecting user identity.

On the other hand, the key problem experienced by consumers is the incorrect stock

information declared on the website.

Future of Online Shopping

According to the interviewees, there is a general perception that online shopping

would become popular in the near future. However, there were some concerns which need to

be overcome which are lack of awareness and ensuring security.

Themes Emerging from Interview Analysis

The analysis carried out in the prior section suggests that following themes emerge

from the qualitative analysis.

Table 2: Themes Emerging from Interviews



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Identified themes can be conceptualised in the following way:

Figure 3: Online Shopping Behaviour of Turkish Consumers

Questionnaire Analysis

The aim of the study was to investigate adoption of internet shopping by Turkish

consumers in the context of Hepsiburada.com. In this context, the factors which determine

online buying behaviour of Turkish consumers were identified. In this regard, the influence of

socio-economic and cultural characteristics of Turkish consumers on their purchases over the

internet were explored and Turkish consumers’ online shopping behaviour were examined by



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scrutinising the influence of beliefs of individuals, their attitudes, subjective norms, and

perceived behavioural control on the utilisation of Hepsiburada.com’s online store through

structured questionnaires. This section presents the findings derived from the analysis of the

questionnaires.

Results

This section presents the results with the help of frequency tables and descriptive

statistics.

Profile of Participants

Table 3: Demographic Characteristic of Respondents

Before exploring the relationship between variables, a set of demographic questions

were asked in order to develop profile of participants (N = 50). According to the table above,

most of the participants were female, 70% of the sample consisted of females and 30%

consisted of males.

More than half of the participants were younger than 35 years old. 8% of the

participants were less than 18 years old while 8% of them reported that they were between the

ages of 18-24. Majority of the participants, 40%, reported that they were between the ages of

25-34, 4% of them stated that they were between the ages of 35-44. 14% of them were

between the ages of 45-54, 16% of them were between 55-64 years old, and lastly 10% of the

participants were 65 and above years old.

Besides, participants were requested to indicate their education level. As it is seen in

the above table, most of the participants had high levels of education. Among participants,

46% of them had postgraduate degree, 30% of them had Bachelor’s degree, 12% of them had



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high school diploma while 4% stated that their education level were less than high school. 4%

of the participants did not provide an answer to this question.

Participants also specified their employment status. More than half of the participants, 68%,

were employed full-time while 10% of the participants were employed part-time. 8% of the

participants were students, 4% of them were retired and 6% of them were unemployed. 4% of

the participants did not provide an answer to this question.

Computer and Internet Usage among Turkish Consumers

In addition to the demographic questions, participants were also requested to indicate

their habits about computer and internet usage.

Table 4: Computer and the Internet Usage Profile of the Participants

Firstly, respondents were requested to specify how long they have been using a

computer. As it is seen in the above table, majority of the participants, 76%, have been using

computer for 6 and more years while 12% of them reported that they have been using

computer for 3 to 5 years. 8% of the participants have been using computer for 1 to 2 years

while 4% of them reported that they do not use computer. Together with computer usage,

participants also indicated how long they have been using internet.

Again, majority of the participants, 62%, have been using internet for 6 and more

years while 22% of them reported that they have been using internet for 3 to 5 years. 8% of



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the participants have been using internet for 1 to 2 years while 4% of them reported that they

do not use internet. 4% of the participants did not provide an answer to this question.

Moreover, respondents indicated how many hours per day they spend on the internet.

36% of the respondents use internet more than 5 hours in a day, 30% of them use internet

between 3 to 4 hours in a day. 18% of them indicated that they use internet between 1 to 2

hours per day while 4% of them stated that they use internet less than an hour per day. 12% of

the participants did not provide an answer to this question.

As it is seen, majority of the participants were very familiar with computer and the

internet and spend time on internet. Participants were also asked to specify where they use

computer and the internet. According to the table above, almost half of the participants, 48%,

use computer and internet at work and home, 40% of them use computer and internet at home

while 4% of them prefer work to use computer and internet. 4% of the participants did not

provide an answer to this question.

Reasons for Using the Internet

Besides, participants were requested to indicate the reasons they use the Internet.

Bar Chart 1: Reasons for Using the Internet

As it is seen, 25% of the participants stated that they use the internet for

communication (i.e. email, chat rooms, and instant messaging), 24% of them stated that they

use the internet to search for information. 16% of the participants reported that they use the

internet for shopping and another 16% stated that they use the internet for personal finances

while 11% of them use the internet to play games. 8% of the participants indicated other

reasons; 4% of them indicated business development as a reason of internet usage, 2% of



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them indicated traffic maps, and 2% of them indicated reading articles as reasons to internet

usage.

Frequency of Online Purchases

Furthermore, participants answered the question how often they use the internet to

make a purchase.

Bar Chart 2: Frequency of Online Purchases

As it is seen in the above bar chart, 4% of them never use the internet to make a

purchase while majority of them, 30%, rarely use the internet for shopping. However, 18% of

the participants reported that they use the internet sometimes to make a purchase, 28% of

them stated that they frequently use the internet for shopping, and 16% of them stated that

they always use the internet for shopping. 4% of the participants did not provide an answer to

this question. It is evident that, more than half of the participants were familiar with online

purchasing.

Goods Bought from the Internet

In investigating participants’ online shopping habits, what items they have purchased

at least one from the internet in the past year were also explored.



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Bar Chart 3: Goods Bought from the Internet

According to the above bar chart, most of the participants, 20%, bought books,

magazines, music, and movies in the past year from the internet; 19% of them bought event

tickets (concert, sports, etc.); 18% of the participants bought travel tickets, 17% of them

bought electronics; 14% of them bought clothes and shoes, 11% of them bought household

items, and 1% of them indicated other item; bought cosmetics from the internet in the past

year.

Awareness of Hepsiburada.com

In later stages, participants were requested to indicate their opinions about

Hepsiburada.com.

Bar Chart 4: Awareness of Hepsiburada.com

First of all, whether they were aware of Hepsiburada.com or not was explored.

According to the above bar chart, most of the participants, 80%, were aware of



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Hepsiburada.com while 8% of them were not aware of Hepsiburada.com. 12% of the

participants did not provide an answer to this question.

Making a Purchase from Hepsiburada.com

Besides, respondents were asked that whether they have ever made purchase from

Hepsiburada.com.

Bar Chart 5: Making a Purchase from Hepsiburada.com

According to the bar chart, most of the participants, 66%, made a purchase from

Hepsiburada.com while 26% of them did not buy any item from Hepsiburada.com. 8% of

the participants did not provide an answer to this question. It is seen that most of the

participants used Hepsiburada.com for shopping.

Amount of Time that Hepsiburada.com Was Used

Further, they answered the question how many times they have used

Hepsiburada.com’s website over the last six months.

Bar Chart 6: Amount of Time that Hepsiburada.com Was Used

According to the bar chart, 22% of the participants have used Hepsiburada.com’s

website once and 36% of the participants have used Hepsiburada.com’s website two to three

times. 4% of them stated that they have used Hepsiburada.com’s website six to seven times

and another 4% of them stated that they have used Hepsiburada.com’s website eight to nine



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times. 34% of the participants did not provide an answer to this question. It is seen that, most

of the participants have bought something from Hepsiburada.com’s website at least once.

Type of Goods Bought from Hepsiburada.com

In specifying participants’ familiarity with Hepsiburada.com, they also asked to

indicate what kind of goods they have purchased from Hepsiburada.com.

Bar Chart 7: Type of Goods Bought from Hepsiburada.com

In specifying their purchased goods, participants indicated various items. According to

the above bar chart, among items bought from Hepsiburada.com, book was stated by 22% of

the participants, CD / DVD and players were stated by 15% of them, and clothing was stated

by 10% of them. 9% of the participants indicated that they bought accessories, another 9% of

them stated that they bought mobile while 8% of them bought camera / video camera from

Hepsiburada.com. Each of cosmetics and jewellery were bought by 5% of the participants and

each of the computer / laptop and cooking devices were bought by 3% of the participants.

Besides, each of the TV, office supply, sport / outdoor, and furniture items were

bought by 2% of the participants. Lastly, each of the tool, sound systems and other goods

(EBook) were stated by 1% of the participants.

Underlying Causes for Purchasing Products from Hepsiburada.com

In this question, participants were asked to state what made them buy from

Hepsiburada.com.



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Bar Chart 8: Underlying Causes for Purchasing Products from Hepsiburada.com

Among the reasons, as it is seen in the bar chart, participants mostly (23%) stated

“price and delivery charges” as reasons to buy from Hepsiburada.com.

“Information about the product (colour, size, content, and picture)” followed; it was

stated by 19% of the participants. 16% of the participants indicated “security offered when

making transactions”, 14% of them indicated “trustworthiness of the web merchant”, 12% of

them indicated “delivery options”, 9% of them indicated “trustworthiness of the web

merchant” as reasons to buy from Hepsiburada.com, and 7% of the participants indicated

other reasons that they prefer purchase from Hepsiburada.com since it offers “products not

available anywhere else”. It is evident that Hepsiburada.com’s qualities cause people to shop

from Hepsiburada.com’s website.

Pros of Hepsiburada.com Compared to Other Online Stores

Besides, participants reported the advantages of buying goods from Hepsiburada.com

as compared to other websites.



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Bar Chart 9: Pros of Hepsiburada.com Compared to Others

Participants indicated various advantages. As it is seen in the above bar chart, “lower

prices” as an advantage of Hepsiburada.com was stated by 17% of the participants, “good

customer service” was stated by 13% of the participants, each of the “after sales service” and

“trustworthiness” were stated by 12% of the participants, “product variety” was stated by 11%

of the participants, each of the “ease of product comparison” and “availability of latest

products” were stated by 8% of the participants, each of the “availability of information (i.e.

customer reviews, etc.)” and “user friendliness of the website” were stated by 7% of the

participants, and lastly “appearance of the website” was stated by 5% of the participants.

Participants agreed that Hepsiburada.com has many advantages over other websites.

12. Cons of Hepsiburada.com

In addition to the advantages, participants also rated the risks of buying goods from

Hepsiburada.com.

Bar Chart 10: Cons of Hepsiburada.com



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According to the above bar chart, “risk of credit card information to be stolen” was

stated as a risk by 21% of the participants, “share of personal information with third parties”

was stated by 20% of the participants, each of the “problems with order confirmation” and

“not able to return easily” were stated by 19% of the participants, “receiving wrong product”

was stated by 13% of the participants, and lastly “problems with payment” was stated as a risk

by 8% of the participants. It seen that, most of the participants were concerned with their

privacy in terms of credit cards and personal information. They were also concerned with

confirmation and delivery of the product. These risks deserve attention in order to make sure

customers and increase shopping rates.

Online Purchase Experience from Hepsiburada.com

Lastly, participants requested to rate their online purchase experience from

Hepsiburada.com.

Bar Chart 11: Online Purchase Experience from Hepsiburada.com

As it is seen in the bar chart, participants’ experiences about Hepsiburada.com was

very good (stated by 46% of the participants), yet 16% of the participants evaluated

Hepsiburada.com as marginal. 38% of the participants did not provide an answer.

Correlation Analysis

In order to determine the relationship between demographic factors, computer and

internet usage, correlation analysis was run. The findings presented in the correlation matrix

below.



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Table 5: Correlation Matrix

According to the findings, among the demographic factors, interestingly, negative

associations are noted between employment status and education (β = -.449, p < .001) and

length of computer usage (β = -.517, p < .001) which indicates that the hypothesis of “higher

education leads higher employment status” is not valid for Turkey.

On the other hand, the findings indicate a positive association between education and

computer usage (β = .324, p < .005). In the case of age, no significant association was found

on computer usage, length and frequency of internet usage but a negative association is noted

on the frequency of online shopping (β = -472, p < .001). This implies that younger Turkish

consumers make more purchases over the internet.

The findings from the correlation analysis also suggest that the frequency of online

shopping is significantly correlated with the length of computer usage (β = .488, p < .001),

length of internet usage (β = .437, p < .001), internet usage per day (β = .387, p < .001) and

actual usage of Hepsiburada.com (β = .366, p < .001). In the same way, internet usage per day

is found to be significantly correlated with the length of computer usage (β =.329, p < .005),

length of internet usage (β = .615, p < .001) and actual usage of Hepsiburada.com’s website (β

= .324, p < .005).

Finally, a strong correlation between computer usage and length of internet usage is

found (β = .627, p < .001). These findings imply that experience with the computer and



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internet facilitates online shopping and determines the frequency of shopping made over the

internet.

However, although these factors positively affect purchase at a particular retailer over

the internet, the strength of the influence declines which may indicate that features of the

website may become prominent.

Regression Analysis

In order to identify the factors predicting behavioural intentions and the actual use of

website series of regression analyses were conducted. The first regression analysis aimed to

identify factors which increase consumers’ attitude towards using Hepsiburada.com for

shopping, the second regression analysis aimed to explore the factors increasing actual usage

of Hepsiburada.com’s website, and the third regression analysis aimed to identify the factors

that drive individuals to add Hepsiburada.com’s website to their favourites.

Regression Output 1 – Attitude 1 vs. Relative Advantage, Compatibility and Complexity

In the first analysis, it was aimed to find out factors predicting consumers’ attitude

towards using Hepsiburada.com’s website for shopping over the internet and the predictor

variables were Relative Advantage, Compatibility, and Complexity of Hepsiburada.com.

Table 6: Regression Output 1 – Attitude 1 vs. Relative Advantage, Compatibility and

Complexity



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In predicting consumers’ attitude towards using Hepsiburada.com’ website for

shopping, as it is seen in Regression Output 1, R value of regression was significantly

different from zero and F (3, 46) = 878.75, p < .001; indicating that the model is significant.

According to the Regression Output 1, predictor variables explained almost all of the

variance, 98%, of attitude towards using Hepsiburada.com.

Explained variance in the regression revealed that, these variables are crucial in

identifying factors affecting customers’ attitude and it can be said that these variables are

enough to explain attitudes towards using Hepsiburada.com.

As it is seen in the Regression Output 1, relative advantage of Hepsiburada.com over

other websites significantly and positively predicted consumers’ attitude towards using

Hepsiburada.com’ website for shopping, it had regression coefficient that was significantly

different from zero (β = .49, p < .001). The size and the direction of the relationship suggested

that increasing advantages of Hepsiburada.com will lead to increases in people’s preferences

for shopping from Hepsiburada.com. Therefore, the advantages of Hepsiburada.com over

other websites should be determined and even should be increased so as to make people shop

from Hepsiburada.com. In addition, compatibility of Hepsiburada.com significantly and

positively predicted consumers’ attitude towards using Hepsiburada.com’s website for

shopping, it had regression coefficient that was significantly different from zero (β = .44, p <

.001). The size and the direction of the relationship suggested that if Hepsiburada.com’s

website fits well with customers’ lifestyle, their attitude to make purchases from

Hepsiburada.com will increase. The website should be designed according to the profile of the

customers in order to increase compatibility between website and customers’ lifestyle which

in turn will increase shopping.

Since attitudes can be determinants of behaviour, increasing relative advantage and the

compatibility of the Hepsiburada.com’s website can drive individuals to make purchases from

Hepsiburada.com. However, the complexity of the website had no effect on customers’

attitude, it had regression coefficient that was not different from zero.



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Regression Output 2 – Actual Usage vs. Plan to Use, Intent to Use and Favourite

In the second regression analysis, it was aimed to find out factors predicting actual

usage of Hepsiburada.com’s website for shopping over the internet and the predictor variables

were plan to use, intent to use, and intent to add Hepsiburada.com to favourite links.

Table 7: Regression Output 2 - Actual Usage vs. Plan to Use, Intent to Use and Favourite

In predicting consumers’ actual use of Hepsiburada.com’ website for shopping, R

value of regression was significantly different from zero and F (3, 46) = 17.73, p < .001;

indicating that the model is significant. Predictor variables explained 54% of variance.

Explained variance in the regression revealed that, even though these variables are important

in identifying factors affecting customers’ actual usage of Hepsiburada.com, there are still

some other factors to be identified.

As it is seen in the Regression Output 2, only intent to add Hepsiburada.com to

favourite links significantly and positively predicted consumers’ actual usage of

Hepsiburada.com’s website for shopping, it had regression coefficient that was significantly

different from zero (β = 1.26, p < .05). The size and the direction of the relationship suggested

that if individuals have intent to add Hepsiburada.com to their favourite links they are likely

to use Hepsiburada.com for shopping.



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A link noticing individuals to “add this page to favourites” can increase individuals’

tendency to use Hepsiburada.com for shopping. However, customers’ plan to use and intent to

use Hepsiburada.com for shopping had no effect on consumers’ actual use of

Hepsiburada.com’s website for shopping. They had regression coefficient that were not

different from zero.

Regression Output 3 – Favourite vs. Subjective Norms, Perceived Behavioural Control,

Relative Advantage and Compatibility

In the last regression analysis, it was aimed to find out factors predicting the intent to

add Hepsiburada.com to favourite links and the predictor variables were subjective norms,

perceived behavioural control, relative advantage, and compatibility of Hepsiburada.com.

Table 8: Regression Output 3 – Favourite vs. Subjective Norms, Perceived Behavioural

Control, Relative Advantage and Compatibility

In predicting consumers’ intent to add Hepsiburada.com to their favourite links, R

value of regression was significantly different from zero and F (4, 45) = 242.05, p < .001



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indicating that the model is significant. Almost all of the variance, 96%, was explained by

predictor variables. Explained variance in the regression revealed that, these variables are

crucial in identifying factors affecting consumers’ intent to add Hepsiburada.com to their

favourite links. It can be said that these variables are enough to explain intent to add

Hepsiburada.com to favourite links.

As it is seen in the Regression Output 3, only compatibility of Hepsiburada.com

significantly and positively predicted intent to add Hepsiburada.com to favourite links, it had

regression coefficient that was significantly different from zero (β = .70, p < .05). The size

and the direction of the relationship suggested that if Hepsiburada.com’s website is

compatible with customers’ lifestyle, consumers are likely to add Hepsiburada.com to their

favourite links. The website of Hepsiburada.com should be well designed to fit well with

customers’ lifestyle in order to make individuals to add Hepsiburada.com to their favourite

links.

Since, adding Hepsiburada.com’s website to favourites increases the actual usage of

Hepsiburada.com for shopping; compatibility of the website bears crucial importance.

However, customers’ subjective norms and perceived behavioural control, and relative

advantage of Hepsiburada.com over other websites had no effect on consumers’ tendency to

add Hepsiburada.com’s website to their favourites. They had regression coefficient that were

not different from zero. It seems that, most of the variance in the regression was explained by

compatibility of Hepsiburada.com’s website with customers’ lifestyle.

Discussion on Findings and Conclusion

In this study, it was aimed to investigate adoption of internet shopping by Turkish

consumers in the context of Hepsiburada.com. Findings suggested that online shopping

behaviour of Turkish consumers was shaped by the national culture and their socio-economic

characteristics. With regards to socio-economic factors, this study supports literature such as

Hubona and Kennick (1996), Donthu and Garcia (1999) and Trocchia and Janda (2000) by

finding that young Turkish consumers are more likely to make online purchases. On the other

hand, this study did not find any supporting evidence regarding the influence of gender on

online shopping and therefore supports the findings of Shin (2009). However, it was also

identified that education level of the respondents did not affect the online consumer behaviour

although it has an effect on the computer usage of Turkish people.



129

In addition, higher employment status was also found as having no effect on the online

consumer behaviour of Turkish people. Chau and Hu (2002) claim the existence of a negative

association between levels of income and individual perception of risk leading to increased

online purchases. However, findings of this study did not confirm this behaviour. However,

findings suggest that in Turkish people who have knowledge on and habit of using computers

and the internet are more inclined to purchase over the internet when compared to people who

are not familiar with these concepts.

These findings validate the results of Ganesan (1994), Novak et al. (2000) and Park

and Kim (2003) and imply that innovativeness of Turkish consumers drives developments of

positive attitudes toward online shopping.

It is evident from the statements of the interviewees that uncertainty avoidance was

highly influential on their attitude and behaviour towards online shopping since low tolerance

to dissatisfaction with purchased products along with fear of being subject to credit card fraud

prohibits consumers to develop positive attitudes and therefore they prevent Turkish

consumer to have favourable behaviour towards online stores. Based on these findings, once

can argue that cultural aspects have influence over the individuals in terms of online

purchasing tendencies (Park and Kim, 2003), relating the findings to the phenomenon that

according to the collectivist culture, online purchasing is concerned risky. Therefore, despite

their demographic and intellectual characteristics, the Turkish people consider shopping over

the internet as risky due to the cultural influence of the collectivism.

The most influential three concerns over shopping online are related to the risk

element, whereas the other drivers are related heavily to the people’s perceptions. The

consideration of the risk element is the evidence of the influence of cultural backgrounds of

consumers since the Turkish people are concerned as risk aversive representatives of the

collectivist mind set (Hofstede, 2001).

The findings from the regression analysis imply that predicting behavioural intentions,

that is consumers’ attitude towards using Hepsiburada.com’ website for shopping over the

internet, the relative advantage of Hepsiburada.com over other websites and compatibility of

the webpage (fit between webpage and consumers’ life style) had significant effects.

If Hepsiburada.com has advantages over others and is compatible with consumers’

lifestyle it is likely to be preferred by consumers. It was also identified that actual users of

Hepsiburada.com are those who add this retailer’s website to their favourites meaning that if

consumers add Hepsiburada.com to their favourite list, they are very likely to use it. Since



130

intent to add Hepsiburada.com to favourites determined the actual use of the website, it was

decided to identify factors which determine the intent to add Hepsiburada.com to favourites

list.

Compatibility of the website had a significant effect meaning that if the website is

designed compatible with consumers’ life style, they are likely to add the website to their

favourites list which in turn makes individuals use Hepsiburada.com for online shopping.

Therefore it can be said that the website should be designed according to the profile of the

customers in order to increase compatibility between website and customers’ lifestyle which

in turn will increase shopping. Since attitudes can be determinants of behaviour, increasing

relative advantage and the compatibility of the Hepsiburada.com’s website can drive

individuals to make purchases from Hepsiburada.com.

These findings imply that when selecting a particular retailer, consumers’ choices are

influenced by the compatibility of the website with the lifestyle of consumers so that it is

added to the favourite list. Findings implied that the Theory of Planned Behaviour can explain

Turkish consumers’ online shopping behaviour which means their actual behaviour is shaped

by their attitudes toward online shopping where attitudes emerge in the form of compatibility

with the lifestyle of consumers.

References

Ajzen, I (1985) ‘From intentions to actions: a theory of planned behaviour’ in Kuhl, J and

Beckman, J (eds), Action-Control: From Cognition to Behavior, Springer, Heidelberg

Alreck, P and Settle, R (2002) ‘The hurried consumer: time-saving perceptions of internet and

catalogue shopping’, Journal of Database Marketing, 10:1, 25-35

Anandarajan, M, Simmers, C and Igbaria, M (2000) ‘An exploratory investigation of the

antecedents and impact of internet usage: an individual perspective’, Behavior and

Information Technology, 19:1, 69-85

Babin, B, Darden, W and Griffin, M (1994) ‘Work and/or fun: measuring hedonic and

utilitarian shopping value’, Journal of Consumer Research, 20: 4, 644-656

Bandura, A (1977) Social Learning Theory, Prentice-Hall, Englewood Cliffs

Bazeley, P (2009) ‘Analysing qualitative data: more than ‘identifying themes’’, Malaysian

Journal of Qualitative Research, 2:2, 6-22

Bellman, S, Lohse, GL and Johnson, EJ (1999) ‘Predictors of online buying behaviour’,

Communications of the ACM, 42:12, 32-38

Bhatnagar, A, Misra, S and Rao, HR (2000) ‘On risk, convenience and internet shopping

behaviour’, Communications of the ACM, 43:11, 98-105

Bigne, E, Ruiz, C, Aldas, J and Sanz, S (2008) ‘Influence of online shopping information

dependency and innovativeness on internet shopping adoption’, Online Information

Review, 32:5, 648-667



131

Bigne-Alcaniz, E, Ruiz-Mafe, C, Aldas-Manzano, J and Sanz-Blas, S (2008) ‘Influence of

online shopping information dependency and innovativeness on internet shopping

adoption’, Online Information Review, 32:5, 648-667

Blaikie, N (1993) Approaches to Social Enquiry, Polity Press, Cambridge

Blaikie, N (2000) Designing Social Research, Polity Press, Cambridge

Booth, WC, Colomb, GG and Williams, JM (2008) The Craft of Research (3rd

edn), The

University of Chicago Press, Chicago

Cavana, R, Delahaye, BL and Sekeran, U (2001) Applied Business Research: Qualitative and

Quantitative Methods, John Wiley & Sons, New York

Chau, PYK and Hu, JH (2002) ‘Investigating healthcare professionals’ decisions to accept

telemedicine technology: an empirical test of competing theories’, Information &

Management, 39: 4, 297-311

Collis, J and Hussey, R (2009) Business Research: A Practical Guide for Undergraduate and

Postgraduate Students (3rd

edn), Palgrave Macmillan, Basingstoke

Dahlen, M (2002) ‘Learning the web: internet user experience and response to web marketing

in Sweden’, Journal of Interactive Advertising, Available from

http://jiad.org/vol3/no1/dahlen/index.htm

Davis, FD, Bagozzi, RP and Warshaw, PR (1989) ‘User acceptance of computer technology: a

comparison of two theoretical models’, Management Science, 35:8, 982-1002

Donthu, N and Garcia, A (1999) ‘The internet shopper’, Journal of Advertising Research, 39:3,

52-59

Fishbein, M and Ajzen, I (1975) Belief, Intention and Behavior: An Introduction to Theory and

Research, Addison-Wesley, Reading

Ganesan, S (1994) ‘Determinants of long-term orientation in buyer–seller relationships’,

Journal of Marketing, 58:2, 1-19

Goldsmith, RE (2001) ‘Using the domain specific innovativeness scale to identity innovative

internet consumers’, Internet Research, 11:2, 149-158

Goldsmith, RE and Bridges, E (2000) ‘E-Tailing versus retailing: using attitudes to predict

online buying behaviour’, Quarterly Journal of Electronic Commerce, 1:3, 245-253

Harrison, AW and Rainer, RK (1992) ‘The influence of individual differences on skill in end-

user computing’, Journal of Management Information Systems, 9:1, 93-111

Hernandez, B, Jimenez, J and Martin, MJ (2011) ‘Age, gender and income: do they really

moderate online shopping behaviour?’, Online Information Review, 35:1, 113-133

Hirschman, E (1980) ‘Innovativeness, novelty seeking and consumer creativity’, Journal of

Consumer Research, 7:3, 283-295

Hofstede, G (2001) Culture’s Consequences: Comparing Values, Behaviours, Institutions and

Organizations across Nations (2nd

edn), Sage Publications, Thousand Oaks

Hubona, GS and Kennick, E (1996) ‘The impact of external variables on information

technology usage behavior’, IEEE Proceedings of the Hawaii International Conference

on System Sciences, IEEE Press, 4: Los Alamitos, 166-175

Im, I, Kim, Y and Han, HY (2008) ‘The effects of perceived risk and technology type on users’

acceptance of technologies’, Information & Management, 45:1, 1-9

Joines, J, Scherer, C and Scheufele, D (2003) ‘Exploring motivations for consumer web use and

their implications for e-commerce’, Journal of Consumer Marketing, 20:2, 90-108

Korgaonkar, P and Wolin, L (1999) ‘A multivariate analysis of web usage’, Journal of

Advertising Research, 38:1, 7-21

Kuisma, T, Laukkanen, T and Hiltunen, M (2007) ‘Mapping the reasons for resistance to

internet banking: a means-end approach’, International Journal of Information

Management, 27:2, 75-85



132

Lee, MC (2009) ‘Factors influencing the adoption of internet banking: an integration of TAM

and TPB with perceived risk and perceived benefit’, Electronic Commerce Research

and Applications, 8:3, 130-141

Li, N and Kirkup, G (2007) ‘Gender and cultural differences in internet use: a study of China

and UK’, Computers & Education, 48:2, 301-317

Liang, ARD and Lim, WM (2011) ‘Exploring the online buying behavior of specialty food

shoppers’, International Journal of Hospitality Management, 30:4, 855-865

Miyazaki, AD and Fernandez, A (2001) ‘Consumer perceptions of privacy and security risks

for online shopping’, The Journal of Consumer Affairs, 35:1, 27-44

Novak, T, Hoffman, D and Yung, Y (2000) ‘Measuring the customer experience in online

environments: a structural modelling approach’, Marketing Science, 19:1, 22-42

Oliver, RL (1997) Satisfaction: A Behavioral Perspective on the Consumer, McGraw Hill, New

York

Pallant, J (2005) SPSS Survival Manual: A Step By Step Guide to Data Analysis Using SPSS for

Windows (2nd

edn), Open University Press, Maidenhead

Park, C and Kim, Y (2003) ‘Identifying key factors affecting consumer purchase behaviour in

an online shopping context’, International Journal of Retail & Distribution

Management, 31:1, 16-29

Polonsky, MJ and Waller, DS (2010) Designing and Managing a Research Project: A Business

Student’s Guide (2nd

edn), Sage Publications, Thousand Oaks

Ramaswami, S, Strader, T and Brett, K (2001) ‘Determinants of online channel use for

purchasing financial products’, International Journal of Electronic Commerce, 5:2, 95-

118

Rangaswamy, A and Gupta, S (1999) ‘Innovation adoption and diffusion in the digital

environment: some research opportunities’, E-Business Research Center Working Paper

02-1999, Penn State University, University Park, Available from

http://www.smeal.psu.edu/cdt/ebrcpubs/res_papers/1999_02.pdf

Rousseau, DM, Sitkin, SB, Burt, RS and Camerer, C (1998) ‘Not so different after all: a cross-

discipline view of trust’, Academy of Management Review, 23:3, 393-404

Saunders, M, Lewis, P and Thornhill, A (2009) Research Methods for Business Students (5th

edn), FT/Prentice Hall, Harlow

Shin, DH (2009) ‘Towards an understanding of the consumer acceptance of mobile wallet’,

Computers in Human Behavior, 25:6, 1343-1354

Solomon, MR (1999) Consumer Behaviour (4th

edn), Prentice Hall, Englewood Cliffs

Sun, H and Zhang, P (2006) ‘The role of moderating factors in user technology acceptance’,

International Journal of Human-computer Studies, 64:2, 53-78

Taylor, S and Todd, PA (1995) ‘Assessing IT usage: the role of prior experience’, MIS

Quarterly, 19:4, 561-570

Trocchia, PJ and Janda, S (2000) ‘A phenomenological investigation of internet usage among

older individuals’, Journal of Consumer Marketing, 17:7, 605-616

Wilson, J (2010) Essentials of Business Research: A Guide to Doing Your Research Project,

Sage Publications Ltd, London

Wolfinbarger, M and Gilly, MC (2001) ‘Shopping online for freedom, control and fun’,

California Management Review, 43:2, 34-55

Yu, J, Ha, I, Choi, M and Rho, J (2005) ‘Extending the TAM for a t-commerce’, Information &

Management, 42:77, 965-976

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